Image Classifier Training

ABSTRACT

Methods are disclosed that include: (a) applying a first stain to a first sample having a plurality of regions, where the first stain selectively binds to only a first subset of the regions of the first sample; (b) applying a second stain to the first sample, where the second stain binds to a second set of regions of the first sample; (c) obtaining an image of the first sample, and analyzing the image to obtain a first component image corresponding substantially only to spectral contributions from the first stain, and a second component image corresponding substantially only to spectral contributions from the second stain; and (d) training a classifier to identify regions of a second sample based on information derived from the first and second component images, the identified regions corresponding to the first subset of regions of the first sample.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of and claims priority to U.S.application Ser. No. 12/486,718, filed on Jun. 17, 2009, which claimspriority to U.S. Provisional application Ser. No. 61/073,222, filed onJun. 17, 2008. The entire contents of each of the foregoing applicationsare incorporated herein by reference.

TECHNICAL FIELD

This disclosure relates to sample imaging and classification.

BACKGROUND

Automated machine-based classifiers can be used to identify features ofinterest in a sample based on one or more sample images. Classifiertraining can be a time-consuming process and can be prone to operatorerror.

SUMMARY

In a first aspect, the disclosure features a method that includes: (a)applying a first stain to a first sample having a plurality of regions,where the first stain selectively binds to only a first subset of theregions of the first sample; (b) applying a second stain to the firstsample, where the second stain binds to a second set of regions of thefirst sample; (c) obtaining an image of the first sample, and analyzingthe image to obtain a first component image corresponding substantiallyonly to spectral contributions from the first stain, and a secondcomponent image corresponding substantially only to spectralcontributions from the second stain; and (d) training a classifier toidentify regions of a second sample based on information derived fromthe first and second component images, the identified regionscorresponding to the first subset of regions of the first sample.

Embodiments of the method can include any of the following features.

The first subset of regions and the second set of regions can haveregions in common. The second set of regions can include at least someregions that are each adjacent to a region in the first subset. Thesecond set of regions can be a subset of the regions of the firstsample. The second set of regions can correspond to substantially all ofthe first sample. The first subset of regions can correspond tomembranes in the first sample. The second set of regions can correspondto nuclei in the first sample. The second set of regions can correspondto non-specific binding sites in the first sample.

The information derived from the first component image can includeposition information corresponding to the first subset of regions. Theinformation derived from the first component image can includeinformation about an amount of the first stain in the first subset ofregions. The information derived from the second component image caninclude information about an amount of the second stain in the secondset of regions.

The method can include applying a third stain to the first sample, wherethe third stain selectively binds to a third subset of the regions ofthe first sample. The first subset of regions and the third subset ofregions can have regions in common. The third subset of regions and thesecond set of regions can have regions in common. The second set ofregions can include at least some regions that are each adjacent to aregion in the third subset of regions.

The method can include analyzing the image to obtain a third componentimage, where the third component image corresponds substantially only tospectral contributions from the third stain, and training the classifierbased on information derived from the third component image.

The second stain can include a counterstain.

The method can include applying a fourth stain to the first sample,where the fourth stain binds to a fourth set of regions of the firstsample. The fourth set of regions can be a subset of regions of thefirst sample. The fourth set of regions can correspond to substantiallyall of the first sample. The method can include analyzing the image toobtain a fourth component image, where the fourth component imagecorresponds substantially only to spectral contributions from the fourthstain, and training the classifier based on information derived from thefourth component image.

The classifier can be a first classifier, and the method can includetraining a second classifier to identify regions of another sample, theidentified regions corresponding to the third subset of regions of thefirst sample.

The first subset of regions of the first sample can include one or moretypes of tissue. The first subset of regions of the first sample caninclude one or more types of cells.

One of the first and second stains can include a fluorogenic stain andthe other of the first and second stains can include a chromogenicstain. The first stain can include at least one member of the groupconsisting of immunohistochemical agents, cytokeratin, cadherin, DAB,fast red, fluorescein, rhodamine, Texas red, Cy3, Cy5, Cy5.5, Alexadyes, and quantum dots. The second stain can include at least one memberof the group consisting of DAPI, Hoechst blue, and hematoxylin.

Analyzing the image can include unmixing the image to obtain the firstand second component images. Analyzing the image can include separatingimage color channels to obtain the first and second component images.

A difference between a wavelength of a maximum in a fluorescenceemission spectrum of the first stain and a wavelength of a maximum in afluorescence emission spectrum of the second stain can be 50 nm or more.A difference between a wavelength of maximum absorption of the firststain and a wavelength of maximum absorption of the second stain can be50 nm or more.

The method can include obtaining birefringence information about thefirst sample, and training the classifier based on the birefringenceinformation. The birefringence information can include one or morebirefringence images of the first sample.

The method can include providing position information corresponding tothe first subset of regions of the first sample to the classifier totrain the classifier. The position information can include an image ofthe first sample that does not correspond to spectral contributions fromthe first stain. The position information can include textureinformation derived from the image of the first sample.

Obtaining the image can include exposing the first sample to incidentradiation and detecting emitted radiation from the first sample, theemitted radiation corresponding to fluorescence emission from the firstsample, reflected incident radiation from the first sample, ortransmitted incident radiation from the first sample.

The method can include using the trained classifier to identify theregions of the second sample.

Embodiments of the method can also include any of the other method stepsor features disclosed herein, as appropriate.

In another aspect, the disclosure features a method that includes: (a)applying a stain to a first sample having a plurality of regions, wherethe stain selectively binds to a first set of regions of the firstsample; (b) obtaining an image of the first sample, and analyzing theimage to obtain a component image corresponding substantially only tospectral contributions from the stain; (c) obtaining a birefringenceimage of the first sample; and (d) training a classifier to identifyregions of a second sample based on information derived from thecomponent image and the birefringence image.

Embodiments of the method can include any of the following features.

The first set of regions can be a subset of the regions of the firstsample. The first set of regions can correspond to substantially all ofthe first sample.

The birefringence image can include a set of regions corresponding to asecond subset of the regions of the first sample, the second subset ofregions of the first sample having optical retardance values that aredifferent from optical retardance values of other regions of the firstsample. The second subset of regions and the first set of regions canhave regions in common.

The first set of regions can include at least some regions that are eachadjacent to a region in the second subset of regions. The second subsetof regions can correspond to membranes in the first sample. The secondsubset of regions can correspond to nuclei in the first sample.

The information derived from the birefringence image can includeposition information corresponding to the second subset of regions.

The second subset of regions can include one or more types of tissue.The second subset of regions can include one or more types of cells. Thesecond subset of regions can include one or more types of sub-cellularcomponents.

The stain can include a counterstain.

Analyzing the image can include unmixing the image to obtain thecomponent image. Analyzing the image can include separating image colorchannels to obtain the component image.

The method can include applying a second stain to the first sample,where the second stain binds to a third set of regions of the firstsample, analyzing the image to obtain a second component imagecorresponding to the second stain, and training the classifier based oninformation derived from the second component image. The second staincan include a counterstain.

The method can include using the trained classifier to identify theregions of the second sample.

Embodiments of the method can also include any of the other method stepsor features disclosed herein, as appropriate.

In a further aspect, the disclosure features a method that includes: (a)applying a stain to a first sample having a plurality of regions, wherethe stain selectively binds to only a first subset of the regions of thefirst sample; (b) obtaining an image of the first sample, and analyzingthe image to obtain a first component image corresponding substantiallyonly to spectral contributions from the first stain, and a secondcomponent image corresponding substantially only autofluorescencecontributions from the sample; and (c) training a classifier to identifyregions of a second sample based on information derived from the firstand second component images, the identified regions corresponding to thefirst subset of regions of the first sample.

Embodiments of the method can include any of the following features.

The method can include applying a second stain to the first sample,where the second stain selectively binds to a second subset of theregions of the first sample.

The method can include analyzing the image to obtain a third componentimage, where the third component image corresponds substantially only tospectral contributions from the second stain, and training theclassifier based on information derived from the third component image.The information derived from the first component image can includeposition information corresponding to the first subset of regions. Theinformation derived from the first component image can includeinformation about an amount of the first stain in the first subset ofregions.

The method can include using the trained classifier to identify theregions of the second sample.

Embodiments of the method can also include any of the other method stepsor features disclosed herein, as appropriate.

In another aspect, the disclosure features an apparatus that includes anelectronic processor configured to obtain the image of the first sample,analyze the image, and train the classifier according to any of themethods disclosed herein. Embodiments of the apparatus can include anyof the features disclosed herein, as appropriate.

In a further aspect, the disclosure features an apparatus that includesan electronic processor configured to obtain the image of the firstsample, analyze the image, obtain the birefringence image of the firstsample, and train the classifier according to any of the methodsdisclosed herein. Embodiments of the apparatus can include any of thefeatures disclosed herein, as appropriate.

Unless otherwise defined, all technical and scientific terms used hereinhave the same meaning as commonly understood by one of ordinary skill inthe art to which this disclosure belongs. All publications, patentapplications, patents, and other references mentioned herein areincorporated by reference in their entirety. In case of conflict, thepresent specification, including definitions, will control. In addition,the materials, methods, and examples disclosed herein are illustrativeonly and not intended to be limiting.

The details of one or more embodiments are set forth in the accompanyingdrawings and description. Other features and advantages will also beapparent from the description, drawings, and claims.

DESCRIPTION OF DRAWINGS

FIG. 1 is a flow chart showing steps in a procedure used to train anautomated classifier.

FIG. 2 is a flow chart showing steps in a further procedure used totrain an automated classifier.

Like reference symbols in the various drawings indicate like elements.

DETAILED DESCRIPTION

Automated analysis of samples can be used for both qualitativeinspection and quantitative determination of sample properties. Forexample, automated analysis of biological samples such as stained tissuesections can be used to measure the amount of one or more stains thatare applied to, and are bound to, the samples. The applied stains canreveal, for example, one or more properties or states of the sample(e.g., tissue) or components thereof (e.g., cells). Analysis protocolscan include, for example, identifying one or more selected regions of asample from one or more images of the sample. The regions can correspondto different types of cells and/or to cells having one or moreparticular types of sub-cellular compartments, for example. Quantitativemeasurements of the identified regions can provide information about theamount of one or more stains present in the identified regions.

Typically, analysis of sample images is performed by an automatedclassifier that has been trained to operate on images that arerepresentative of the sample images. Automated classifiers can embodyvarious types of machine learning algorithms, including geneticalgorithms, neural networks, and vector support machines. Methods andsystems for automated classification of sample images are disclosed, forexample, in the following patents and patent applications, the entirecontents of each of which are incorporated herein by reference: U.S.Pat. No. 7,555,155 to Richard Levenson et al., issued on Jun. 30, 2009;and U.S. patent application Ser. No. 12/401,430 to Kirk William Gossageet al., filed on Mar. 10, 2009. To ensure that throughput is high and toreduce or eliminate the effects of operator error, such automatedclassifiers can typically classify samples with little or no operatorintervention once they have been properly trained. That is, oncetrained, the classifiers can identify portions of sample imagescorresponding to particular regions of a sample either fullyautomatically or semi-automatically. Once the portions of the imageshave been identified, the classifiers can also perform quantitativeanalyses corresponding to the regions of the sample with little or nooperator intervention.

In some systems, automated classifiers are trained using manual methods.Typically, for example, a trained expert such as a pathologist manuallyselects regions of one or more images corresponding to particularregions of interest in a sample (e.g., particular types of cells,particular cellular components, particular tissue types and/or targetstructures), for example by drawing outlines around the regions on adisplay screen. In some types of samples, there may be several differenttypes of cell components, tissue types, and/or structures of interestfor the operator to manually select. The samples corresponding to theimages classified manually by the operator may include one or morestains applied to the samples to delineate different regions in thesamples (e.g., cancer cells versus stroma) and/or to delineate differentportions of individual cells (e.g., cytoplasm and nuclei). Conventionalcounterstains that are typically applied to such samples providecontextual information to trained operators that the operators can usein manually classifying regions of the samples. Counterstains that canbe used to stain such samples include hematoxylin and eosin, forexample.

Classifier training can be prone to inaccuracies arising fromdifferences in perception and conclusions among differentoperators—different operators might classify different pixel subsets oftraining images as belonging to different classes (e.g., different typesof cells, different cellular components). Further, training automatedclassifiers using images that are manually classified can be timeconsuming. For example, to train a neural network-based classifier, manydifferent images may have to be manually classified by a systemoperator, with multiple regions in each of the images carefully selectedand indicated as belonging to one of the classes of interest (or notbelonging to a class of interest). The set of images used to train theautomated classifier typically includes a range of different types ofregions of interest to effectively represent the bounds of the sampleimages to be automatically analyzed. To develop a training set ofclassified images, particularly when the samples under study areexpected to include considerable variability (e.g., for tissue samplescorresponding to clinical cancer sections), large numbers of regionscorresponding to each sample class of interest (e.g., 100 or moreregions) may be selected manually. If these regions are improperlyselected, training of the automated classifier may be inaccurate,leading to inaccurate image analysis results.

Further, direct determination of regions of samples based on appliedstains may not be possible. For example, suitable stain(s) may not beavailable for the tissue types under investigation. As an example,cytokeratin can be used to locate cytoplasm in breast cancer cells. Ingeneral, however, there are no cytokeratins that are universal, and thatuniformly stain all breast cancer tissues. Instead, to identifycytoplasm in multiple different types of breast cancer cells, multipledifferent cytokeratins (e.g., selected from a list of about 20cytokeratins) may be used. A more general method for identifying breastcancer cell membranes would be particularly useful, for example, incertain quantitative assessments where a computed metric corresponds tothe percentage of membrane area where an applied stain is present, ineach identified breast cancer cell. Unfortunately, the absence ofgeneralized stains for such purposes hinders this method of analysis.

Some attempts to classify samples have involved the application ofimmunohistochemical (IHC) agents to the samples, which can targetparticular portions of samples due to particular molecular specificitiesand add high visual contrast to the targeted portions. Simple imageanalysis algorithms such as thresholding can then be used to identifythe targeted portions in sample images. Such agents are available, forexample, from HistoRX (New Haven, Conn.).

The use of IHC agents for this purpose is restricted by a number ofconsiderations, however. In some samples, for example, only a limitednumber of IHC agents can be applied before specificity for particularportions is lost. Further, a significant fraction of the multiplexingbandwidth of microscopy systems used to obtain sample images may bededicated to measurement of spectral responses of the applied IHCagents. As a result, these dedicated spectral channels are typically notavailable for obtaining other information, such as information aboutparticular types of molecular expression within the sample. Thus, fromboth the perspective of IHC agent specificity and from spectralconsiderations, this approach can limit or reduce the amount ofmolecular information that can be obtained.

The methods and systems disclosed herein are designed to provideapproaches to training automated classifier systems that reduce and/oreliminate the manual labor involved in classifying training image sets,and that are generally applicable to a wide variety of different sampletypes. To accomplish this, in some embodiments, the methods and systemsinclude applying spectrally distinguishable stains in two differentstain preparations to samples, and then acquiring spectral images of thesamples. By performing spectral unmixing on the acquired images, atleast two different component images can be obtained. The componentimages can then be used directly to train automated classifier systems.

FIG. 1 shows a flow chart 100 that includes a series of steps fortraining an automated classifier. In steps 110 and 120, two differentstain preparations are applied to a training sample (e.g., a tissuesection). In some embodiments, steps 110 and 120 can be combined so thatthe two stain preparations are applied in a single step. Typically, thestain preparations include applying stains that have different spectraland chemical properties. For example, the first stain preparation, whichcan include one or more individual steps, typically applies one or moreregional markers to the sample; each of the applied regional markersselectively binds to particular regions of interest in the sample. Eachof the regional markers in the first stain preparation is generallychosen so that it binds preferentially to one class of region ofinterest (e.g., where there is more than one class of region ofinterest) in the sample. Multiple different regional markers in thefirst stain preparation can be used to target different classes ofregions of interest in the sample. In general, each of the differentregional markers present in the first stain preparation are specific(e.g., bind preferentially) to a different particular type of region ofinterest in the sample. Regions of interest can include tissuestructures, cells, sub-cellular compartments, and/or regions wherechemical and/or biological agents are localized within the sample. Byapplying the first stain preparation to the sample, the regions ofinterest (or multiple classes or regions of interest) in the sample aredelineated from other regions that are not of interest.

A wide variety of different markers can be used in the first stainpreparation. Exemplary markers include, for example, IHC agents, one ormore cytokeratins, cadherin, chromogenic markers such as DAB and/or fastred, fluorescent markers such as fluorescein, rhodamine, Texas red, Cydyes such as Cy3, Cy5, and Cy5.5, Alexa dyes, and quantum dots thatfluoresce at any selected wavelength. Mixtures of any of these agentscan be used in combination in the first stain preparation. Inparticular, combinations of both fluorogenic and chromogenic agents canbe used in the first stain preparation to identify specific regions ofthe sample. In some embodiments, some or all of the different markersused in the first stain may be applied more lightly than they wouldotherwise be applied in histological staining procedures to ensure thatthe various spectral contributions of the different markers can beseparated from one another.

The second stain preparation typically includes one or morenon-localized markers such as counterstains, and can include one or morestaining steps to apply the non-localized markers to the sample.Exemplary non-localized markers that can be applied in the second stainpreparation include hematoxylin, DAPI, and Hoechst blue. Although inFIG. 1 the second stain preparation is applied following the first stainpreparation, more generally, the first and second stain preparations canbe applied in any order. Further, in certain embodiments, application ofthe first and second stain preparations can be interleaved, such thatsteps involved in applying both regional markers and non-localizedmarkers to the sample are performed in succession.

In some embodiments, the first and second stain preparations areselected such that some or all of the markers applied to the sample ineach stain preparation are spectrally distinct. For example, the maximain the total absorption or emission spectrum of the markers applied inthe first and second stain preparations, respectively, can be separatedby 5 nm or more (e.g., 10 nm or more, 20 nm or more, 30 nm or more, 50nm or more, 75 nm or more, 100 nm or more, 200 nm or more, 300 nm ormore).

In step 130 of flow chart 100, the stained sample (e.g., with the firstand second stain preparations applied) is exposed to incident radiation,and one or more multispectral images (e.g., a spectral cube of data)corresponding to the sample is/are measured. The spectral cubecorresponds to a plurality of two-dimensional images of the sample, withper-pixel spectrally resolved information extending along the thirddimension of the cube. The spectrally resolved information can include,for example, sample absorption information as a function of wavelength,sample emission information as a function of wavelength, samplereflectance information as a function of wavelength, or other types ofspectral information.

In step 140, the spectral cube is unmixed to obtain sample componentimages. In some embodiments, if the responses of particular markersapplied in the first and second stain preparations are sufficientlyseparated from one another spectrally, the “unmixing” can be performedsimply by examining different spectral sub-regions of the spectrallyresolved information corresponding individually to the particularmarkers to obtain component images of the sample. In certainembodiments, the responses of the markers may partially overlapspectrally; the spectral overlap may even be significant or complete insome embodiments. To obtain component images, the spectral cube of datacan be unmixed to obtain component images corresponding substantiallyonly to contributions from the particular markers, respectively. Methodsand systems for spectral unmixing are disclosed, for example, in thefollowing patents and patent application publications, the entirecontents of each of which are incorporated herein by reference: PCTApplication No. PCT/U.S. 2004/031609 to Richard Levenson et al., filedon Sep. 23, 2004, published as WO 2005/040769; and U.S. Pat. No.7,321,791 to Richard Levenson et al., issued on Jan. 22, 2008. Suitablesystems for obtaining multispectral images of samples and performingunmixing of such images to obtain individual component images includethe Nuance® system available from Cambridge Research & Instrumentation(Woburn, Mass.).

Although the preceding and subsequent discussion relates to acquiringspectral images of stained samples and unmixing the spectral images,other methods can also be used to obtain sample images that can be usedto train automated classifiers, and to classify samples using trainedclassifiers. For example, one or more color images of a sample (e.g.,red-green-blue (“RGB”) images) can be acquired. Color images includedifferent color channels (e.g., separate red, green, and blue channels)that can be independently analyzed, and the information from theseseparate channels can be used to train classifiers and classify samplesusing trained classifiers. Sample images corresponding to the individualcolor channels (or combinations of the channels) can form the componentimages that would otherwise be obtained from spectral unmixing (see thesubsequent discussion of steps 140 and 150 in FIG. 1), particularlywhere regional markers applied in the first stain preparation aregenerally spectrally distinct from other regional and/or non-localizedmarkers, and correspond spectrally to particular color channels.Alternatively, or in addition, the color channels (or color “planes”)from one or more RGB image(s) can be used as the inputs to an unmixingstep, to obtain component images. Accordingly, in the preceding andsubsequent discussion, steps that involve acquiring spectral images andunmixing the images to obtain component images can include,alternatively or in addition, acquiring one or more color images andusing images corresponding to different color channels, either with orwithout spectral unmixing of images corresponding to the different colorchannels, to train automated classifiers.

Spectral unmixing corresponds to a linear decomposition of an image orother data set into a series of contributions from different spectralcontributors. Images of the stained samples disclosed herein willinclude multiple different contributions, including contributions frommarkers applied to the sample in each of the first and second stainpreparations. Each of these contributions can be unmixed or decomposedinto a separate spectral channel, forming an image of the stained samplethat corresponds almost entirely to signal contributions from singlespectral sources. When the contributions are unmixed into separatechannels or images, signal strengths can be accurately quantified andanalyzed.

The numerical spectral unmixing procedure will be described below for atissue section that is stained with a single fluorescent stain. Theequations can be generalized in straightforward fashion to includespectral contributions from multiple markers. The spectral data recordedat a given point (x,y) in an image depends on the amount of signal fromthe stain and on tissue autofluorescence as:

S(x, y, λ)=a(x, y)* F(λ)+b(x,y)*G(λ)   [1]

where (x, y) indices are used to denote a given pixel location in theimage, the asterisk “*” denotes multiplication, λ is used to denote agiven wavelength of fluorescence emission or detection, and

S(x, y, λ) denotes the net signal for a given location and wavelength,

F(λ) denotes the emission spectrum of autofluorescence,

G(λ) denotes the emission spectrum of the stain,

a(x, y) indicates the abundance of autofluorescence signal at a given(x, y) location, and

b(x, y) indicates the abundance of stain fluorescence at a given (x, y)location.

Equation [1] states that the net signal from a given location is the sumof two contributions, weighted by the relative amount ofautofluorescence and stain fluorescence present. It is easier to see ifone writes the above equation for a single pixel:

S(λ)=a F(λ)+b G(λ)   [2]

F and G may be termed the spectral eigenstates for the system, which arecombined in various amounts according to the amount of autofluorescenceand stain emission, to produce an observed spectrum S.

Now if the emission spectra of the autofluorescence and of the stain areknown (or can be deduced), one may invert equation [2] by linear algebrato solve for a and b, provided that the spectrum S has at least twoelements in it, i.e., that one has data for at least two emissionwavelengths λ. Then we can write

A=E ⁻¹ S   [3]

where

A is a column vector with components a and b, and

E is the matrix whose columns are the spectral eigenstates, namely [FG].

Using equation [3], one can take a captured spectral image (e.g., aspectral cube) and calculate the abundance of the autofluorescence andof the stain sources. This process can be repeated for each pixel in theimage, to produce separate images of the sample that correspondsubstantially to autofluorescence only, and to stain fluorescence only,and are free of contributions from other spectral sources. Note that thematrix E need only be inverted once for a given set of autofluorescenceand stain spectra, so the calculation of abundances is not burdensomeand can be readily done in nearly real-time by a personal computer.

In some embodiments, when multiple stains are applied to a sample, theindividual spectra (e.g., the spectral eigenstates discussed above) ofthe stains are different than the spectra of the stains appliedindividually to tissue sections. These changes can arise, for example,from chemical interactions between the various stains, and/or fromenvironmental conditions during or after the staining protocol. As longas these changes can be quantitatively reproduced in control experimentsto provide accurate spectral eigenstates for the unmixing algorithm,however, the individual contributions of these stains to spectral imagesof the sample can be deconvolved to obtain quantitative informationabout the absolute amount of each stain present in the tissue section.

Accordingly, by using the spectral unmixing methods discussed above instep 140 of flow chart 100, component images of the sample correspondingto regional and non-localized markers applied in the first stainpreparation and in the second stain preparation, respectively, can beobtained. When the first stain preparation includes multiple regionalmarkers (e.g., when the multiple regional markers are used toselectively stain different classes of regions of interest in thesample), the spectral unmixing process can yield separate componentimages corresponding substantially only to contributions from each ofthe single regional markers. When the second stain preparation includesmultiple non-local markers, the spectral unmixing process can yieldseparate component images corresponding substantially only tocontributions from each of the single non-localized markers.

Component images corresponding to regional markers applied to the samplein the first stain preparation identify where regions of interest in thesample are located. By identifying these regions through a combinationof selective staining and spectral unmixing, the tedious, time-consumingstep of manual classification of regions of interest in sample images totrain an automated classifier can be reduced or eliminated. Inembodiments where multiple different classes of regions of interest arepresent, each of the multiple unmixed component images corresponding toone of the regional markers applied in the first stain preparation canbe used to identify portions of the sample corresponding a different oneof the classes of regions of interest.

Component image(s) corresponding to one or more regional markers appliedin the first stain preparation can be submitted to the automatedclassifier in step 150. Analysis of the component image(s), usingtechniques such as comparison against fixed and/or adaptive thresholds,can be used to establish regions of interest in the sample for trainingthe classifier.

Component image(s) corresponding one or more non-localized markersapplied in the second stain preparation can also be submitted to theautomated classifier in step 150. The component image(s) correspondingto the non-localized marker(s) are analyzed using informationdelineating regions of interest from the one or more component imagescorresponding to regional markers applied in the first stain preparationto train the automated classifier in step 150 to classify regions inimages that correspond to samples stained with non-localized markerssuch as those applied in the second stain preparation. For example,classification algorithms implemented in the automated classifier can bedeveloped to determine whether each pixel in an image belongs to aparticular class of region of interest, or even to one of severalclasses of regions of interest. Typically, information derived fromcomponent images corresponding to regional markers applied in the firststain preparation is used to identify regions of interest in sampleimages, and then the one or more component image(s) corresponding tonon-localized markers applied in the second stain preparation (e.g., oneor more counterstains) is/are used to train the automated classifier,guided by the identified regions of interest.

This training procedure, in step 160, yields a trained classifier, whichcan then be used to identify regions of interest in subsequent samplesthat have been stained, for example, only with non-localized markerssuch as those applied in the second stain preparation (e.g., one or morecounterstains). Further, the trained classifier can be used to identifyregions of interest in samples that have been stained using one or moreregional markers; the regional markers can be the same as, or differentfrom, regional markers that are applied in the first stain preparation.In some embodiments, for example, subsequent samples are not stainedwith regional markers such as those applied in the first stainpreparation or with any other regional marker, but are instead stainedwith one or more non-localized stains such as one of more of the stains(e.g., counterstains) applied in the second stain preparation and/or oneor more other non-localized stains. The trained automated classifier canidentify and classify regions of interest in such samples based oncomponent images corresponding to the applied non-localized stains(e.g., corresponding to one or more of the counterstains that wereapplied to the training sample in the second stain preparation).

Typically, the types of regions of interest in subsequent samplescorrespond to the types of regions of interest identified in the sampleused to train the automated classifier. For example, the regions ofinterest in the training sample can include a first type of region ofinterest, such as a first type of tissue, for which a selective regionalmarker is available. The regional marker is applied to the trainingsample in the first stain preparation in flow chart 100, and a trainedautomated classifier is obtained in step 160. Subsequently, the trainedautomated classifier is used to analyze one or more samples that includea second type of regions of interest different from the first type ofregion of interest. The second type of regions of interest in thesubsequent samples correspond to the first type of regions of interestin the training sample. This procedure can be used where no selectiveregional marker is readily available for the second type of region ofinterest. For example, in identifying and classifying certain types ofcancers, one or more regional markers may be available for a firstphenotype, and a training sample that includes the first phenotype canbe used to train the automated classifier. Subsequently, samples thatinclude a second phenotype that corresponds to, but is not identical to,the first phenotype can be analyzed using the trained automatedclassifier. Although the regions of interest in the training sample andthe subsequent samples are of different types, if they are sufficientlyrelated, then the automated classifier can still be used to identify thesubsequent samples. As a result, cells, sub-cellular components,structures, and other sample features for which no selective regionalmarker is reliably available can nonetheless readily be identified andanalyzed.

In certain embodiments, the training procedure shown in flow chart 100,implemented by applying a particular set of regional markers to atraining sample in the first stain preparation, can yield a trainedautomated classifier that is capable of identifying and analyzingregions of interest in subsequent samples to which different regionalmarkers have been applied. The different regional markers may not beapplied for purposes of training the classifier, but can instead beapplied to selectively stain certain portions of the samples for visualanalysis, or for automated analysis using simple methods such asthresholding detection. This procedure can be used in situations wherethe subsequent samples have a range of variability that might otherwiserequire applying multiple different regional markers and/or mightotherwise require selection among several different regional markers toproperly classify the sample, and/or in situations where samples areincompatible with the use of multiple different regional markers.

In certain embodiments, one or more birefringence images of a sample canbe used in place of, or in addition to, the one or more regional markersapplied in the first stain preparation to train an automated classifier.FIG. 2 shows a flow chart 200 that includes a series of steps that canbe used to train an automated classifier using birefringenceinformation. In step 210, a first stain preparation is applied to thesample. In optional step 220, a second stain preparation can be appliedto the sample. Then, in step 230, the stained sample is exposed toincident radiation and one or more images of the sample are obtained.The one or more images can include, for example, multispectral imagesthat include a spectral cube of data corresponding to absorption,reflectance, or emission of radiation by the stained sample is obtained.In some embodiments, the one or more images can include one or morecolor images (e.g., RGB images) that include multiple color channels,each of which can be analyzed separately, as discussed previously. Theone or more markers that are applied to the sample in each of the firstand second stain preparations in steps 210 and 220, respectively, aretypically non-localized stains similar to those discussed previously inconnection with the second stain preparation applied in step 120 of thechart shown in FIG. 1.

In step 240, the stained sample is exposed to further incidentradiation, and one or more birefringence images of the sample areobtained. In the procedure shown in FIG. 2, the one or morebirefringence images of the sample provide information about regions ofinterest in the sample in a manner similar to the regional markersapplied in the first sample preparation discussed in connection withFIG. 1. Birefringence images show image contrast where components of asample are birefringent. For example, certain sample components such ascollagen have natural birefringence that manifests as image intensitymodulation in birefringence images. The intensity modulation (e.g.,intensity differences with respect to intensities of non-birefringentsample components) can be used to delineate regions of interest in thesample corresponding to the birefringent components.

In optional step 250, the images obtained in step 230 can be unmixed toobtain component images corresponding to each of the one or morenon-localized markers applied to the sample in step 210 and optionalstep 220. In some embodiments, the one or more images obtained in step230 are not unmixed, and are instead submitted directly to theclassifier as “component” images (e.g., if the first stain preparationincludes only one or two non-localized markers, and no second stainpreparation is applied to the sample).

The component images (which can include non-unmixed images, as discussedabove) corresponding to markers applied in the first stain preparationand, optionally, to markers applied in the second stain preparation, areused along with the birefringence image(s) in step 260 to train theclassifier. Regions of interest during the training are identified andanalyzed based on the birefringence image information, and theclassifier is trained by analyzing component images corresponding to thenon-localized markers based on the regions of interest. In step 370, atrained automated classifier is obtained. Once trained, the automatedclassifier can be used as discussed in any of the previous embodimentsto analyze samples that have been stained with regional markers,non-localized markers, and mixtures of regional and non-localizedmarkers. The automated classifier can also be used to classifysubsequent samples on the basis of one or more birefringence images ofthe samples in addition to, or as an alternative to, applying any of theregional and/or non-localized markers. Typically, when the automatedclassifier is trained based on regions-of-interest information derivedfrom one or more birefringence images, the trained classifier can beused to identify and analyze regions of interest in birefringence imagesof subsequent samples with or without non-localized stains such ascounterstains applied to the samples.

A variety of different methods and systems can be used to acquirebirefringence images of samples. Exemplary methods and systems aredisclosed, for example, in U.S. patent application Ser. No. 11/397,336to Clifford C. Hoyt et al., filed on Apr. 4, 2006, published as U.S.Patent Application Publication No. U.S. 2007/0231784, the entirecontents of which are incorporated herein by reference.

As discussed above, step 220 in flow chart 200 is optional. Typically,one or more additional non-localized stains such as counterstains mightbe applied where particular sample architectural information is revealedby such stains. Visual review and assessment of classified samples(e.g., by a system operator following classification by the automatedclassifier) may also be aided by the application of additionalnon-localized stains in step 220. Even if such stains are applied instep 220, component images corresponding to the stain(s) may not, insome embodiments, be used to train the automated classifier. Instead,component images corresponding to the one or more non-localized markersapplied in the first stain preparation in step 210 may be used to trainthe classifier.

In certain embodiments, rather than applying a second stain preparationto the training sample (including one or more non-localized markers),the automated classifier can be trained based on autofluorescenceinformation obtained from one or more sample images. For example,referring again to FIG. 1, a first stain preparation can be applied tothe training sample, the first stain preparation including one or moreregional markers as in step 110. The stained sample, without applying asecond stain preparation, can then be exposed to radiation, and one ormore spectral images of the sample can be obtained in step 130. Byunmixing the spectral images in step 140, a component imagecorresponding to sample autofluorescence can be obtained, along withcomponent images corresponding to each of the different regional markersapplied in step 110. One or more of the component images correspondingto the regional markers can be used, along with the autofluorescenceimage of the sample, to train the classifier in step 150. Because sampleautofluorescence is typically emitted from multiple different regions ofa sample, the autofluorescence image can perform a function duringclassifier training that is analogous to component images thatcorrespond to non-localized markers. In step 160, a trained automatedclassifier is obtained and can be used to classify other samples basedon autofluorescence images (or even other types of images), for example.

In certain other embodiments, rather than applying a second stainpreparation to the training sample, the automated classifier can betrained based on birefringence information obtained from one or moresample images. For example, referring again to FIG. 1, a first stainpreparation can be applied to the training sample, the first stainpreparation including one or more regional markers as in step 110. Thestained sample, without applying a second stain preparation, can then beexposed to radiation, and one or more spectral images of the sample canbe obtained in step 130. A birefringence image is also obtained. Byunmixing the spectral images in step 140, a component image is obtainedfor each of the regional markers applied in step 110. Unmixing can beomitted if the markers are spectrally distinct enough. One or more ofthe component images corresponding to the regional markers can be used,along with the birefringence image of the sample, to train theclassifier in step 150. The birefringence image can perform a functionduring classifier training that is analogous to component images thatcorrespond to non-localized markers. In step 160, a trained automatedclassifier is obtained which can be used to classify other samples basedon birefringence images.

In some embodiments, information about regions of interest in a samplecan be provided and used to train an automated classifier, where suchinformation would otherwise be difficult to obtain by applying certainregional markers to a training sample. For example, one or more regionalmarkers can be applied to a sample in the first stain preparation andcomponent images corresponding to the regional markers can be used toidentify regions of interest in the sample. An automated classifier canthen be trained based on the identified regions. Images of subsequentsamples can then be acquired by methods that are incompatible withstaining the sample with the regional markers used in the first stainpreparation. Such incompatibilities can arise for a number of reasons,including spectral overlap between markers used in the first stainpreparation and markers applied to the subsequent samples, and chemicalor biological incompatibilities between markers used in the first stainpreparation and markers applied to the subsequent samples. A number ofsignificant advantages can arise from using such a procedure. In someembodiments, for example, the analysis of subsequent samples canincluding applying a larger number of markers (e.g., non-localizedmarkers) than might otherwise be possible if regional markers wereapplied to the samples. Further, regional markers that might beincompatible with the regional markers used in the first stainpreparation, but which yield useful sample information, can be appliedin the analysis of subsequent sample. Further still, by appropriateselection of the markers applied to subsequent samples, spectralchannels that would otherwise be dedicated to measurement ofcontributions from certain regional markers (e.g., those used in thefirst stain preparation) can be freed and used to obtain additionalsample information.

In some embodiments, some of the regions-of-interest informationsupplied to the automated classifier (e.g., in the form of componentimages in step 150 of flow chart 100) can be instead supplied directly(e.g., automatically or by a system operator) to the automatedclassifier to delineate regions of interest in the training sample. Thedirect provision of information can occur, for example, when particularregions of interest in a sample are optically and/or biochemicallyincompatible with regional markers. Alternatively, or in addition,regions-of-interest information can be supplied to the automatedclassifier when the identification of the regions, e.g., by applyingregional markers to the sample, would require the application of toomany markers, leading to spectral overlap and/or chemical or biologicalincompatibilities between the applied markers.

In certain embodiments, a classifier can be trained to identify andanalyze several different classes of regions of a sample. For example, aclassifier can be trained to identify and analyze classes of regionsthat correspond to multiple different tissue types. Alternatively, aclassifier can be trained to identify and analyze classes of regionsthat correspond to multiple different cell types, multiple differentsub-cellular structures, multiple different tissue structures or types(e.g., inflamed tissue and non-inflamed tissue), or multiple differentdisease states. For example, a regional marker applied to the trainingsample in the first stain preparation might localize in each of two ormore different classes of regions of interest, such as two or moredifferent tissue types. A system operator can intervene in the trainingprocess to indicate which of the regions in which the regional markerlocalizes belong to each class of tissue. Alternatively, or in addition,an electronic processor can apply an algorithm to assign the identifiedregions into classes (e.g., tissue types) based on criteria such as thepositions of the regions, for example.

In some embodiments, the methods disclosed herein can be used to trainmore than one automated classifier. For example, in some embodiments,the methods disclosed herein can be used to train a first classifierthat identifies and analyzes a first type of region in samples, and asecond classifier that identifies and analyzes a second type of region.The first classifier can be trained to identify certain types of tissueor certain types of cells or certain types of sub-cellular components ofcells. Similarly, the second classifier can be used to identify othertypes of tissue or cells or sub-cellular components. As another example,a first classifier can be trained to identify a certain type of tissue,and a second classifier can be trained to identify a certain type ofcells and/or sub-cellular components. As yet another example, a firstclassifier can be trained to identify a certain type of cells, and asecond classifier can be trained to identify a certain type ofsub-cellular components. In general, any number of automated classifierscan be trained to identify a variety of different types of regions,structures, and components of samples.

In certain embodiments, as discussed above, more than one non-localizedmarker (e.g., counterstain) can be applied to a training sample (e.g.,in step 120 of flow chart 100). Multiple non-localized markers can beapplied, for example, when multiple classifiers are trained usinginformation derived from a single training sample. As another example,multiple non-localized markers can be applied to highlight certainfeatures of samples and/or to provide additional visual information to asystem operator. Typically, the multiple non-localized markers areselected such that they are spectrally distinct, but markers thatoverlap spectrally can be unmixed using the methods disclosed herein. Instep 140 of flow chart 100, for example, if multiple non-local markersare applied to a sample, then spectral unmixing will yield multiplecomponent images corresponding to the non-localized markers. One or moreof these component images can be used in step 150 to train an automatedclassifier, depending upon the nature of the sample images that theautomated classifier will be used to analyze.

In the methods and systems disclosed herein, the extent of overlapbetween regions of the sample to which the regional markers bind andregions to which the non-localized markers bind can vary considerably.For example, in some embodiments, the first stain preparation includes aregional marker that binds to cancerous membranes of cells in a tissuesample. The second stain preparation includes a non-localized markerthat binds to nuclei of cells in the tissue sample.

Some of the nuclei to which the non-localized marker binds correspond tocancerous cells, and other nuclei do not. Different component images,derived for example by unmixing spectral images of the stained sampleand/or by analyzing independent channels of a multi-color sample image,corresponding substantially only to contributions from the regionalmarker, and from the non-localized marker, respectively, can be used totrain an automated classifier to identify and classify cancerousmembranes.

As another example, as discussed above, an automated classifier can betrained based on component images corresponding to one or more regionalmarkers applied in the first stain preparation, and based on a componentimage corresponding to sample autofluorescence which is typicallyemitted from most or all portions of the sample. The trained classifiercan then be used to classify samples based on autofluorescence images ofthe samples, and/or based on component images corresponding to othernon-localized markers (e.g., which typically bind to a large number ofnon-specific binding sites within samples).

More generally, regional markers (or their functional equivalents, suchas sample birefringence) typically bind or correspond to a first subsetof regions within a sample. Non-localized markers (or their functionalequivalents, such as sample autofluorescence or, in some embodiments,sample birefringence) typically bind or correspond to a second subset ofregions within the sample. The first and second subsets of sampleregions can, in some embodiments, share almost no regions in common.Alternatively, in certain embodiments, the first and second subsets ofsample regions can share one or more regions in common. In someembodiments, the first and second subsets of sample regions can shareall or nearly all regions in common. For example, the second subset ofsample regions can be a superset that includes all of the first subsetof sample regions, and also includes additional sample regions. Further,in certain embodiments, some regions corresponding to the first subsetof regions can be positioned adjacent to some regions corresponding tothe second subset of regions. For example, as discussed above, aregional marker can be used to stain cell membranes. Portions of thesample that correspond to the cell membranes correspond to the firstsubset of regions in the sample. A second, non-localized marker can alsobe applied to the sample, and can bind to regions of the sample such asnuclei which are adjacent to cell membranes. Accordingly, some membersof the second subset of sample regions corresponding to the nucleistained with the non-localized marker can be at least partially adjacentto some members of the first subset of sample regions corresponding tothe cell membranes.

The trained classifiers obtained, e.g., in steps 160 and 270 of FIGS. 1and 2, or following any of the other methods disclosed herein, can beused to identify and classify one or more regions of samples other thanthe training sample. Typically, although not in all embodiments, theother samples are stained with one or more of the non-localized markersapplied to the training sample, one or more images of the stainedsamples are obtained and submitted to the trained classifier, and theclassifier classifies different regions of the samples. In someembodiments, the other samples do not include any of the non-localizedstains applied to the training sample; instead, the other samples caninclude one or more different non-localized stains (e.g.,counterstains), and images of the samples can be unmixed to obtaincomponent images corresponding to the applied non-localized stains.Alternatively, or in addition, the other samples can be classified basedon one or more autofluorescence and/or birefringence images of thesamples.

Machine Hardware and Software

The steps described above in connection with various methods forcollecting, processing, analyzing, interpreting, and displayinginformation from samples, and for training automated classifiers basedon information obtained from samples, can be performed by electronicprocessors (such as computers or preprogrammed integrated circuits)executing programs based on standard programming techniques. Suchprograms are designed to execute on programmable computers orspecifically designed integrated circuits, each comprising a processor,a data storage system (including memory and/or storage elements), atleast one input device, and at least one output device, such as adisplay or printer. The program code is applied to input data to performthe functions described herein and generate output information which isapplied to one or more output devices. Each such computer program can beimplemented in a high-level procedural or object-oriented programminglanguage, or an assembly or machine language.

Furthermore, the language can be a compiled or interpreted language.Each such computer program can be stored on a computer readable storagemedium (e.g., CD ROM or magnetic diskette) that, when read by acomputer, can cause the processor in the computer to perform theanalysis and control functions described herein.

OTHER EMBODIMENTS

A number of embodiments have been described. Nevertheless, it will beunderstood that various modifications may be made without departing fromthe spirit and scope of the disclosure. Accordingly, other embodimentsare within the scope of the following claims.

1. A system, comprising: a support apparatus configured to supportsamples; a radiation source configured to illuminate samples; an imagingapparatus configured to obtain images of samples; and an electronicprocessor connected to the imaging apparatus and configured to: receivean image of a first sample on the support apparatus, the first samplecomprising a first stain and a second stain, wherein the first stainselectively binds to only a first subset of regions of the first sample;analyze the image of the first sample to obtain a first component imagecorresponding substantially only to spectral contributions from thefirst stain, and a second component image corresponding substantiallyonly to spectral contributions from the second stain; and train aclassifier to identify a second subset of regions in a second samplethat corresponds to the first subset of regions in the first sample,wherein the second sample does not comprise the first stain.
 2. Thesystem of claim 1, wherein the electronic processor is furtherconfigured to: receive an image of the second sample on the supportapparatus; analyze the image of the second sample to obtain a thirdcomponent image corresponding substantially only to spectralcontributions from the second stain in the second sample; and use theclassifier to identify the second subset of regions in the second samplebased on the third component image.
 3. The system of claim 1, whereinthe first and second subsets of regions correspond to a common type ofcell or sub-cellular compartment.
 4. The system of claim 1, wherein thefirst and second subsets of regions correspond to a common type oftissue structure.
 5. The system of claim 1, wherein the first and secondsubsets of regions correspond to regions where a common biological orchemical agent is localized.
 6. The system of claim 1, wherein the firstand second subsets of regions correspond to membranes in the first andsecond samples.
 7. The system of claim 1, wherein the first and secondsubsets of regions correspond to cell nuclei in the first and secondsamples.
 8. The system of claim 1, wherein the first stain comprises atleast one member of the group consisting of immunohistochemical agents,cytokeratin, and cadherin.
 9. The system of claim 1, wherein the secondstain comprises at least one member of the group consisting ofimmunohistochemical agents, DAPI, Hoechst blue, hematoxylin, DAB, fastred, fluorescein, rhodamine, Texas red, Cy3, Cy5, Cy5.5, Alexa dyes, andquantum dots.
 10. The system of claim 1, wherein the first samplecomprises a third stain, and wherein the electronic processor is furtherconfigured to analyze the image of the first sample to obtain a thirdcomponent image corresponding substantially only to spectralcontributions from the third stain.
 11. The system of claim 10, wherein:the third stain selectively binds to only a third subset of regions ofthe first sample; the electronic processor is further configured totrain the classifier to identify a fourth subset of regions in thesecond sample that corresponds to the third subset of regions in thefirst sample; and the second sample does not comprise the third stain;12. The system of claim 11, wherein the first and third subsets ofregions of the first sample have at least some portions of the firstsample in common.
 13. The system of claim 1, wherein the electronicprocessor is configured to analyze the image of the first sample byunmixing the image to obtain the first and second component images. 14.The system of claim 1, wherein the electronic processor is configured toanalyze the image of the first sample by separating color channels ofthe image to obtain the first and second component images.
 15. Thesystem of claim 1, wherein the second stain binds unselectively to thefirst sample, and wherein the second component image comprisescontributions from the second stain in substantially all regions of thefirst sample.
 16. The system of claim 2, wherein the second stain bindsunselectively to the second sample, and wherein the image of the secondsample comprises contributions from the second stain in substantiallyall regions of the second sample.
 17. The system of claim 1, wherein theimaging apparatus is configured to obtain images of samples by detectingfluorescence emission from the samples, incident radiation reflected bythe samples, or incident radiation transmitted by the samples.
 18. Thesystem of claim 1, wherein the electronic processor is configured totrain the classifier by providing position information about the firstsubset of regions to the classifier.
 19. The system of claim 18, whereinthe position information comprises texture information derived from theimage of the first sample.
 20. The system of claim 1, wherein theclassifier comprises a machine learning classifier.
 21. The system ofclaim 1, wherein the electronic processor is further configured to:analyze the image of the first sample to obtain a component imagecorresponding to autofluorescence of the first sample; and train theclassifier to identify the second subset of regions in the second samplethat corresponds to the first subset of regions of the first samplebased on autofluorescence information about the second sample.
 22. Asystem, comprising: a support apparatus configured to support samples; aradiation source configured to illuminate samples; an imaging apparatusconfigured to obtain images of samples; and an electronic processorconnected to the imaging apparatus and configured to: receive an imageof a first sample on the support apparatus, the first sample comprisingn selective stains and m non-selective stains, wherein each of the nselective stains binds to only a subset of regions of the first sample;analyze the image of the first sample to obtain (n+m) component images,each of the component images corresponding substantially only tospectral contributions from one of the (n+m) stains; and for at leastone of the n selective stains, train a classifier to identify a subsetof regions in a second sample that corresponds to a subset of regions ofthe first sample to which the at least one selective stain binds,wherein the second sample does not comprise any of the n selectivestains, wherein m≧1 and n≧m.
 23. The system of claim 22, wherein theelectronic processor is further configured to: receive an image of thesecond sample on the support apparatus; analyze the image of the secondsample to obtain a component image corresponding substantially only tospectral contributions from one of the m non-selective stains in thesecond sample; and use the classifier to identify the subset of regionsin the second sample based on the component image corresponding to thesecond sample.
 24. The system of claim 22, wherein the subsets ofregions in the first and second samples correspond to a common type ofcell or sub-cellular compartment.
 25. The system of claim 22, whereinthe subsets of regions in the first and second samples correspond to acommon type of tissue structure.
 26. The system of claim 22, wherein thesubsets of regions in the first and second samples correspond to regionswhere a common biological or chemical agent is localized.
 27. The systemof claim 22, wherein each of the n selective stains comprises a memberof the group consisting of immunohistochemical agents, cytokeratin, andcadherin.
 28. The system of claim 22, wherein each of the mnon-selective stains comprises a member of the group consisting ofimmunohistochemical agents, DAPI, Hoechst blue, hematoxylin, DAB, fastred, fluorescein, rhodamine, Texas red, Cy3, Cy5, Cy5.5, Alexa dyes, andquantum dots.
 29. The system of claim 22, wherein m=1.
 30. The system ofclaim 29, wherein the first and second samples each comprise the nselective stains, and wherein n≧2.
 31. The system of claim 22, whereinm=2.
 32. The system of claim 31, wherein the first and second sampleseach comprise the n selective stains, and wherein n≧2.
 33. The system ofclaim 22, wherein the second sample comprises each of the n selectivestains, and for each one of the n selective stains, the electronicprocessor is configured to train the classifier to identify a subset ofregions in the second sample that corresponds to a subset of regions ofthe first sample to which the each one of the n selective stainsselectively binds.
 34. The system of claim 22, wherein the electronicprocessor is configured to analyze the image of the first sample byunmixing the image to obtain the (n+m) component images.
 35. The systemof claim 22, wherein the electronic processor is configured to analyzethe image of the first sample by separating color channels of the imageto obtain the (n+m) component images.
 36. The system of claim 22,wherein the electronic processor is further configured to: analyze theimage of the first sample to obtain a component image corresponding toautofluorescence of the first sample; and for the at least one of the nselective stains, train the classifier to identify the subset of regionsin the second sample that corresponds to the subset of regions of thefirst sample to which the at least one selective stain binds based onautofluorescence information about the second sample.